Multi-organ segmentation in abdominal Computed Tomography (CT) images is of
great importance for diagnosis of abdominal lesions and subsequent treatment
planning. Though deep learning based methods have attained high performance,
they rely heavily on large-scale pixel-level annotations that are
time-consuming and labor-intensive to obtain. Due to its low dependency on
annotation, weakly supervised segmentation has attracted great attention.
However, there is still a large performance gap between current
weakly-supervised methods and fully supervised learning, leaving room for
exploration. In this work, we propose a novel 3D framework with two consistency
constraints for scribble-supervised multiple abdominal organ segmentation from
CT. Specifically, we employ a Triple-branch multi-Dilated network (TDNet) with
one encoder and three decoders using different dilation rates to capture
features from different receptive fields that are complementary to each other
to generate high-quality soft pseudo labels. For more stable unsupervised
learning, we use voxel-wise uncertainty to rectify the soft pseudo labels and
then supervise the outputs of each decoder. To further regularize the network,
class relationship information is exploited by encouraging the generated class
affinity matrices to be consistent across different decoders under multi-view
projection. Experiments on the public WORD dataset show that our method
outperforms five existing scribble-supervised methods.

基于 CT 图像的腹部多器官分割是诊断腹部病变和后续治疗规划至关重要的一项任务。本研究提出了一种新颖的 3D 框架，通过两种一致性约束方法进行 scribble 辅助的多腹部器官分割，表明该方法优于现有的五种 scribble 辅助方法。

基于涂鸦的三分支多扩张网络的像素级和类别级一致性的多个腹部器官三维分割

Scribble-based 3D Multiple Abdominal Organ Segmentation via  Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency

We focus on an important yet challenging problem: using a 2D deep network to
deal with 3D segmentation for medical image analysis. Existing approaches
either applied multi-view planar (2D) networks or directly used volumetric (3D)
networks for this purpose, but both of them are not ideal: 2D networks cannot
capture 3D contexts effectively, and 3D networks are both memory-consuming and
less stable arguably due to the lack of pre-trained models.
In this paper, we bridge the gap between 2D and 3D using a novel approach
named Elastic Boundary Projection (EBP). The key observation is that, although
the object is a 3D volume, what we really need in segmentation is to find its
boundary which is a 2D surface. Therefore, we place a number of pivot points in
the 3D space, and for each pivot, we determine its distance to the object
boundary along a dense set of directions. This creates an elastic shell around
each pivot which is initialized as a perfect sphere. We train a 2D deep network
to determine whether each ending point falls within the object, and gradually
adjust the shell so that it gradually converges to the actual shape of the
boundary and thus achieves the goal of segmentation. EBP allows boundary-based
segmentation without cutting a 3D volume into slices or patches, which stands
out from conventional 2D and 3D approaches. EBP achieves promising accuracy in
abdominal organ segmentation. Our code has been open-sourced
this https URL

本文提出一种新方法 —— 弹性边界投影（EBP），可以使用二维深度网络处理医学图像分割三维数据。该方法可以在不切分三维体积的情况下实现基于边界的分割，并在腹部器官分割方面取得了良好的准确性。